Modern driver assistance systems are being increasingly equipped with an electronic road sign recognition system in order to, e.g., warn the driver in the event of speeding. For this purpose, a camera acquires images of the region in front of the vehicle and delivers corresponding image data to an onboard computer that analyzes and classifies the image data by means of an algorithm in order to identify a road sign therefrom. Such a method is known from, e.g., DE 198 52 631 A1.
According to known methods for road sign recognition, image regions that may contain potential road signs are identified in the camera image in a detection phase. After that, in a second procedure step, these potential road signs are submitted to a classificator that decides whether a road sign is present in the image region and which road sign it is.
The classificator or classification unit may operate in a learning-based manner, which is known from, e.g., DE 10 2005 062 154 A1 where the classificator is appropriately trained in advance using a set of learning examples whose specific designs depend on the selected detection method. For example, a known method for speed limit recognition consists in searching for circles in the camera image by means of image processing phases during the detection phase and submitting the surrounding rectangle as an image detail to the classificator, wherein this feature “circles” defines a particular class of road signs. Such a method for the recognition of circular objects in image data of an image sensor is known from DE 10 2005 017 541 A1.